\section{Conclusion}

One of our research questions concerned the effect of evolving
complex behaviour using a relatively small population in a
specific environment. We suspected we might end up with niche
populations that would not be different from bots evolved in
a different map. Given the results described in section \ref{results},
we can clearly conclude that the small population, the rather high
impact of `random chance' on which bot comes out on top, and the many
degrees of freedom (many different characteristics) necessary for the
highly complex behaviour of bots are a considerable problem
for evolution. (ref. \ref{reference4}) In a way, we did indeed end up with niche
populations, which in effect were worse than we suspected: they
simply did not evolve very clearly because of the reasons stated
above.
\begin{quote}
"\textit{It is often difficult to build interesting or realistic virtual 
entities and still maintain control over them.}" (Karl Sims, ref. 
\ref{reference3})
\end{quote}

The other research question involved the effect of dependencies
on the evolution of the bots. We ended up not really investigating
this research question because even without dependencies the
evolution did not give us the desired results.

Ironically, the dependencies might be a possible solution for the
problems we encountered, as adding more dependencies reduces the
number of possible variations, which makes the effect of changes
in genotypes on the fitness of bots more pronounced. (Karl Sims, ref. 
\ref{reference3}) Also the way normalizations are performed, a small 
change in the genotypes could lead to a greater change in the phenotypes, 
which would maybe give the bot the chance to stand out in the population. 
This would increase the ease in which the bot would be sorted out to be the 
better one.

Another possible solution for these problems would be to experiment
and try to obtain a fitness function which does a better job at
giving a higher value to bots who have a better chance of winning.
Our current fitness functions (except fitness function 4, which was
a test function) base their judgements on frags, deaths or ranking.
As we have seen however, bots high in the ranking do not have better
genotypes in general. A more complicated fitness function could be
developed which based its judgement on a in-depth analysis of the game
which could provide better results.

One would also like to experiment with more sizable bot pools, but
this is hard to do given limitations in Quake III. To work around
those limitations one would have to implement tournament schedules
in which sets of bots take turns in competing with eachother so as
to simulate an environment with a larger population. This could be
done by using a tournament scheme (as with tennis tournaments)
where the winning bots go on to higher rounds, and where their final
place (round) in the tournament would decide their chances for reproduction.
Also a shift scheme could be developed where each bot would play at least
once against all other bots, and use their average fitness score over
all those rounds.

To return to the research question, another thing that could be improved
by setting specific dependencies is the fun-factor as described in
section \ref{research_question}. The dependencies can enforce certain
rules on the bots' characteristics which should make the bots behave
more realistic (for instance a bot which is more aggressive should
be less of a camper). A genetic algorithm could also be used here to
evolve the bots as opponents to the human player. They would adapt their
style of play to the human player and could choose to prefer certain trade-offs
in the dependencies over others. When the player adapts his/her playstyle, the
bots eventualy will also change their playstyle again. This will provide the
human player with more challenge and diversity in game-play over time. A
note must be made that diversity must be kept within a population, so that
a human player does not have to play to bots who all made the same trade-offs
within the dependencies (and thus the same style of play).
